{"id":400086,"url":"https://alion.io/job/nvidia-customer-success-insights-engineer","title":"Customer Success Insights Engineer","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":820,"ghost_share":0.024,"stale_share":0.685,"repost_share":0.046,"time_to_fill_p50_days":36,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Customer Success","role_family":"Customer Success","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":168000,"max":258750,"currency":"USD","period":"year","gross":null,"usd_annual":258750},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":true},{"name":"Claude Code","optional":true},{"name":"Cursor","optional":true},{"name":"Databricks","optional":true},{"name":"Delta Lake","optional":true},{"name":"GitHub","optional":true},{"name":"GitLab","optional":true},{"name":"LLM","optional":true},{"name":"OpenAI Codex","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"Salesforce","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-08-17T00:00:00Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-29T05:31:06Z","board_verified":true,"closed_at":null,"days_open":73,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":73},"description":"NVIDIA's Customer Success Business Insights team is looking for a Customer Success Insights Engineer to scale platform adoption through data and AI-native analytical solutions. You'll surface how customers, partners, and developers adopt NVIDIA platforms - and where we can accelerate their success. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us!\nWhat you’ll be doing:\nDesign, build, and operate automated data collection and transformation pipelines across enterprise systems, vendor APIs, and public developer platforms into Databricks, with data quality gates, freshness monitoring, and fail-safe behavior built in from day one.\n\nUse AI agents throughout the engineering lifecycle: multi-agent build workflows, automated verification, and adversarial review gates before anything reaches production or an executive audience.\n\nTurn ambiguous adoption questions from leadership into measurable definitions, transparent metrics, and self-serve dashboards, including the caveats: knowing when signals must not be summed, funneled, or over-claimed.\n\nDevelop and maintain executive dashboards and recurring analytical products that track platform adoption, developer engagement, and ecosystem health across NVIDIA software.\n\nPartner with Product, Marketing, Sales Operations, and external platform vendors to source new telemetry, validate data contracts, and establish baselines before changes ship, so every initiative gets a measured before and after.\n\nOperationalize measurement for emerging channels (AI agent marketplaces, developer registries, model hubs) where APIs change weekly and un-captured history is lost forever.\n\nChampion data honesty as a product feature: every number defensible, every source detailed, every anomaly investigated before it reaches a customer.\n\nWhat we need to see:\nBS degree in Computing Science, Engineering, Math or equivalent experience.\n\n8+ years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or similar roles in building production data products.\n\nAgentic AI & LLM Mastery: Proven experience operationalizing Large Language Models (LLMs) into autonomous agents that can plan, use tools, and implement multi-step workflows, applied to real engineering: agent-assisted development, automated verification and review gates, or agentic pipelines that shipped to production.\n\nDatabricks Mastery: Proven deep expertise in Apache Spark, PySpark, Delta Lake, and Databricks Workflows. Hands-on experience scaling Unity Catalog is highly preferred.\n\nExpert SQL and Python, including API-based data ingestion from enterprise systems and third-party platforms.\n\nExperience integrating CRM and enterprise data (Salesforce or similar) with product telemetry into unified analytical models.\n\nA track record of building executive-facing dashboards and analytical narratives that leaders trust and act on.\n\nExcellent communication, stakeholder management, analytical, and problem-solving skills.\n\nWays to stand out from the crowd:\nBackground with NVIDIA AI technologies and platforms, or measurement of developer ecosystems (GitHub/GitLab telemetry, package registries, model hubs, marketplace analytics).\n\nExperience designing multi-agent or agentic engineering workflows (Claude Code, Codex, Cursor, Nemotron, or similar) with verification and code review gates.\n\nActive Databricks Certifications (e.g., Data Engineer Professional, Generative AI Engineer Associate).\n\nMS in Computer Science, Data Science, or equivalent experience in a related professional background.\n\nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5.You will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until October 23, 2026.This posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. 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